Chaotic Neural Network Model for Predicting Relative Humidity in Hotan of Xinjiang Autonomous Region

Abstract

Abstract: According to the insufficiency of a single index in identifying the chaotic character,the Hurst exponent,lyapunov exponent and saturated correlation dimensions were used to identify the chaotic characteristics of the relative humidity in Hotan.The chaotic neural network model was established.The monthly average relative humidity for the period of 1954-2002 was simulated and predicted for the period of 2003-2004.The average relative error was 2.96% for the simulation and 0.85% for the prediction respectively.The validation results indicated that the model had a relative high accuracy.

Key words: Relative humidity, Relative humidity, Hurst exponent, Lyapunov exponent, Saturated correlation dimensions, Chaotic neural network

ZHANG Gao-feng1,2,HUANG Ling-mei1,SHEN Bing1,ZHANG Xiao-wei1,QIN Sheng-ying3(1.Key Lab of Northwest Water Resources and Environmental Ecology,Ministry of Education,Xi'an University of Technology,Xi'an 710048,China; 2.Shaanxi Modern Architecture Design and Research Institute;3.Administration Bureau of Hotan River). Chaotic Neural Network Model for Predicting Relative Humidity in Hotan of Xinjiang Autonomous Region[J]. Chinese Journal of Agrometeorology.

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Chaotic Neural Network Model for Predicting Relative Humidity in Hotan of Xinjiang Autonomous Region

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